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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Luo, Yijing Han, Bo Gong, Chen PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China Jiangsu Key Lab of Image Video Understanding for Social Security China Department of Computer Science Hong Kong Baptist University Hong Kong Department of Computing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9780999241165

Practically, we often face the dilemma that some of the examples for training a classifier are incorrectly labeled due to various subjective and objective factors. Although intensive efforts have been put to design classifiers that are robust to label noise, most of the previous methods have not fully utilized data distribution information. To address this issue, this paper introduces a bi-level learning paradigm termed "Spectral Cluster Discovery" (SCD) for combating with noisy labels. Namely, we simultaneously learn a robust classifier (Learning stage) by discovering the low-rank approximation to the ground-truth label matrix and learn an ideal affinity graph (Clustering stage). Specifically, we use the learned classifier to assign the examples with similar label to a mutual cluster. Based on the cluster membership, we use the learned affinity graph to explore the noisy examples based on the cluster membership. Both stages will reinforce each other iteratively. Experimental results on typical benchmark and real-world datasets verify the superiority of SCD to other label noise learning methods. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Iterative methods

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A compressive sensing approach of multiple descriptions for network multimedia communication

A compressive sensing approach of multiple descriptions for ...

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IEEE Workshop on Multimedia Signal Processing

作者： Liangjun Wang Xiaolin Wu Guangming Shi Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University China Department of Electrical & Computer Engineering McMaster University Canada

ISBN: (纸本)9781424422944;9781424422951

A new multiple description coding (MDC) approach is proposed based on the theory of compressive sensing (CS). The CS theory allows a signal to be reconstructed from a small number of its random measurements if the signal is sparse in some space. An attractive property of CS for MDC applications is that the reconstruction error only depends on the number but not on which of the transmitted measurements that are received. By treating each CS measurement as a description, we have a balanced MDC scheme with fine description granularity and low encoding complexity. Another advantage of the new MDC approach is that all signals can be coded the same but decoded in different spaces for better sparse reconstruction.

关键词： image reconstruction Decoding Loss measurement Multimedia communication image coding Encoding Measurement uncertainty

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Edge-based image coding at low bit-rate

Edge-based image coding at low bit-rate

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IEEE International Conference on image Processing

作者： Yi Niu Xiaolin Wu Guangming Shi Xiaotian Wang Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University China Department of Electrical & Computer Engineering McMaster University Canada

We propose a novel edge-based low bit-rate image codec. Although being edge based, the codec does not explicitly code the edge geometry. To avoid spending high bit budget on edges, a background layer of the image is first coded and transmitted, from which the decoder estimates the trajectories of significant edges. The edge regions are then refined by a residual coding technique based on edge dilation and sequential scanning in the edge direction. Experimental results show that the new image coding technique outperforms existing ones in both objective and perceptual quality, particularly at low bit rates.

关键词： image edge detection Encoding Pixel image coding Transform coding Skeleton image reconstruction

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Optimization of ultrasonic imaging using persistence and filter technology

Optimization of ultrasonic imaging using persistence and fil...

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2013 2nd International Conference on Materials Science and Technology, ICMST 2013

作者： Guo, De Quan Yang, Hong Yu Zhang, Cong Yao Liu, Dong C. Key Laboratory of Fundamental Synthetic Vision Graphics and Image Science for National Defense Sichuan University Sichuan Province China Department of Machinery Engineering Neijiang Vocational and Technical College Neijiang China College of Computer Science Sichuan University Sichuan Province China

ISBN: (纸本)9783037857137

B-mode ultrasonic images are often pervaded by the electronics noise and speckle artifact, which may make the interpretation of medical images difficult. In this paper, a legible method for ultrasonic image is constructed and it can be effective to suppress the noise and improve the images quality. Dynamic persistence technology based on temporal multi-frames is utilized to smooth the electronics noise and even lower speckle artifact. To reduce computing complexity, a sum table method and conjugate direction search approaches are applied to speed up estimation of motion vector. The guided image filter strategy is used to enhance the contrast and detail resolution for the processed image by persistence. Experimental results show that the proposed method is effective to smooth noise and enhance contrast of ultrasound image in vivo. © (2013) Trans Tech Publications, Switzerland.

关键词： Ultrasonic imaging

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Edge direction dispersion and its application in automatic suitable-matching area selection

Edge direction dispersion and its application in automatic s...

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Chinese Control and Decision Conference

作者： Qing-Hai, Ding Xin-Rong, Yu Hai-Bo, Luo Hong-Mei, Liu Research Institute of General Development and Demonstration of Equipment Equipment of Air Force Bejing 100076 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110016 China Graduate School Chinese Academy of Sciences Beijing 100039 China Key Laboratory of Optical-Electronics Information Processing Chinese Academy of Science Shenyang 110016 China Key Laboratory of Image Understanding Computer Vision Liaoning Province 110016 China AVIC HONGDU Aviation Industry Group Ltd. Nanchang 330024 China

ISBN: (纸本)9781424487363

Automatic suitable-matching area selection is an important research in visual guidance, scene matching and local non-texture target tracking. Meanwhile, it is the major research subject in computer vision and accurate guidance. In this paper, a new characteristic quantity, i.e., the edge direction dispersion was defined. Then the computation method, parameters selection and real-time realization of this characteristic quantity were investigated. Furthermore, the application of its characteristic in automatic suitable-matching area selection was studied. With a reasonable design for calculation precision, solving method for arctan(x) function in [-∞,+∞ ] could be simplified to linearization calculations in [0, 0.25], then a fast algorithm for arctan(x) function was presented. Experimental results showed that the sub-image extracted by our fast algorithm had higher matching level and was suitable to be a matching template. All the calculations involved in the proposed matching algorithm could be done in one filed period and the real-time request could be satisfied. Moreover, this characteristic of edge direction could also be used to measure the comparability of template and real-time images, which could be widely used in automatic target recognition and target tracking. © 2011 IEEE.

关键词： Target tracking

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A cooperative immune coevolutionary algorithm for multi-objective optimization

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Tien Tzu Hsueh Pao/Acta Electronica Sinica 2014年第5期42卷 858-867页

作者： Qi, Yu-Tao Liu, Fang Ren, Yuan Liu, Jing-Le Jiao, Li-Cheng School of Computer Science and Technology Xidian University Xi'an Shaanxi 710071 China Institute of Intelligent Information Processing Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an Shaanxi 710071 China

According to the distribution characteristics of the Pareto set (PS) of multi-objective optimization problems (MOPs), a cooperative coevolutionary model with new problem decomposition method was designed. By introducing the proposed coevolutionary model into artificial immune system, a cooperative immune coevolutionary algorithm for multi-objective optimization (CICAMO) was proposed. In CICAMO, the Tchebycheff decomposition method is employed to divide sub-populations at first, and then linear probabilistic models are built for each sub-population to piecewise approximate the distribution of the whole PS. In antibody reproducing step, two types of approaches based on clonal selection and model sampling are employed. Experimental results indicate that CICAMO can achieve a good performance in terms of both solution quality and convergence rate, especially when solving MOPs with non-linear relationship between decision variables.

关键词： Multiobjective optimization

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The Infrared Multispectral image Simulation Research Based on 3D Scene

The Infrared Multispectral Image Simulation Research Based o...

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2013 3rd International Conference on Electric and Electronics(EEIC 2013)

作者： Bo Li Huai-ci Zhao Mei Du Jinfeng Lv Department of Optical-Electronics and Information Processing Shenyang Institute of automation Chinese Academy of Science University of Chinese Academy of Science Key Laboratory of Optical-Electronics Information Processing Chinese Academy of Science Key Laboratory of Image Understanding and Computer Vision Shenyang Institute of Engineering

A method of generating infrared multispectral simulation images by 3D scene is presented. The infrared images at any observation angle of the scene and a data cube of simulation images of specific waveband could be generated by the method. Some experiments have been done in waveband 3～5μm to generate a data cube involving 401 infrared simulation images in the wavelength step 0.005μm, and the experiments results show that the simulation method presented in this paper could generate infrared multispectral images data cube rapidly. A way to reflect the differences while the observation angle changed is mentioned in this paper.

关键词： infrared multispectral simulation the data cube self-radiation

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Edge-based dynamic ROI coding with standard compliance

Edge-based dynamic ROI coding with standard compliance

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IEEE Workshop on Multimedia Signal Processing

作者： Yi Niu Xiaolin Wu Guangming Shi Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University China Department of Electrical & Computer Engineering McMaster University Canada

An edge-based region-of-interest (ROI) image coding technique is proposed for low bit-rate visual communication. An image is compactly coded into two semantic levels: background sketch and object textures. The background sketch is a down-sampled version of the input image. The decoder reconstructs the background first by adaptive interpolation and lets users identify the ROI, and then requests the ROI textures to be transmitted. A distinct advantage of the proposed ROI technique is a compact edge-based descriptor of natural object boundaries. The expensive ROI geometry computations are carried out at the encoder only on demand, keeping decoder complexity low to benefit wireless devices. The new system outperforms the dynamic ROI coding of JPEG 2000 in both visuality and PSNR. Furthermore, both background and texture coding can be made compliant with any existing compression standard.

关键词： image coding Decoding Bit rate Visual communication image reconstruction Collaboration Large-scale systems Communication standards Laboratories computer science education

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Research advances on sparse cognitive learning, computing and recognition

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Jisuanji Xuebao/Chinese Journal of computers 2016年第4期39卷 835-852页

作者： Jiao, Li-Cheng Zhao, Jin Yang, Shu-Yuan Liu, Fang Xie, Wen Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education International Research Center for Intelligent Perception and Computation Xidian University Xi'an710071 China School of Computer Science and Technology Xidian University Xi'an710071 China

Sparse Cognitive Learning, Computing and Recognition is a widespread concern front filed by international academic circles in recent years, this new paradigm will have be a revolutionary impact on the fields of machine learning, pattern recognition, computational intelligence,big data etc. In order to better grasp the law of development, three forms of this new paradigm, such as sparse coding, structured sparse and hierarchy sparse, are reviewed in detail from the perspective of biological visual cognitive mechanism and model structure in this paper. In the meantime, outlined the application research of this computational model and exemplified some concrete achievements. Furthermore, some existing problems and research directions are discussed by combining with the current application research. In order to arouse interested researcher pay more attention to this new paradigm. © 2016, Science Press. All right reserved.

关键词： Artificial intelligence

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Blind deconvolution for single noisy and blurry image using alternating maximum a posteriori estimation with low rank prior

Blind deconvolution for single noisy and blurry image using ...

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International Symposium on Communications and Information Technologies (ISCIT)

作者： Shijie Sun Huaici Zhao Jinfeng Lv Mingguo Hao Bo Li Key Laboratory of Image Understanding and Computer Vision Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Optical-Electronics Information Processing Chinese Academy of Sciences Shenyang China College of Information Shenyang Institute of Engineering Shenyang China

ISBN: (纸本)9781509041008

The purpose of single image blind deconvolution is to estimate the unknown blur kernel from a single observed blurred image and recover the original sharp image. Such task is severely ill-posed and even more challenging especially in the condition that the noise in the input image cannot be negligible. In this paper, the main problem we focus on is how to effectively apply low rank prior to blind deconvolution. A single noisy and blurry image blind deconvolution algorithm is proposed, using alternating maximum a posteriori (MAP) estimation combined with low rank prior. When estimating the intermediate latent image, low rank prior is used as the constraint that is used for noise suppression of the restored image. The denoised intermediate latent image in turn leads to higher quality blur kernel estimation. These two operations are iterated in this manner to arrive at reliable blur kernel estimation. Extensive experiments show the superiority of the proposed method over state-of-the-art techniques, both qualitatively and quantitatively.

关键词： C# languages

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